肾透明细胞癌M2型巨噬细胞新型预后预测模型的开发与验证

Development and validation of a novel prognosis prediction model for M2-type macrophage of clear cell renal cell carcinoma

目的建立一个共表达M2相关基因的预后风险评估模型并阐明M2巨噬细胞在肾透明细胞癌(ccRCC)免疫微环境中的作用。方法转录组数据、临床数据和突变数据来自TCGA-KIRC。使用CIBERSORT计算539个样本中每个样本的M2巨噬细胞比例。通过共表达确定了TCGA-KIRC中与M2巨噬细胞相关的基因并建立共表达网络。使用LASSO回归构建预后模型并将结果显著的因素输入Cox回归分析。使用ArrayExpress数据库中的外部数据集E-MTAB-1980并建立风险评分及相应生存曲线。通过基质免疫浸润、GSEA、TMB和药物敏感性对相关模型风险评分进行评估。结果获得TCGAKIRC中与巨噬细胞M2相关性最强的前46个基因,富集于免疫受体活性、白细胞和单核细胞迁移等过程。建立了一个由12个巨噬细胞M2相关基因组成的模型,并证明该模型具有良好的预后能力。M2巨噬细胞浸润与肿瘤代谢密切相关且与ccRCC的风险评分成反比。结论提出了一个ccRCC的M2型巨噬细胞相关的十二基因Cox比例危险回归模型,该模型可为生成ccRCC患者预后评分提供一种测量方法。

Objective To establish a prognostic risk assessment model for coexpressed M2 related genes and to elucidate the role of M2 macrophages within the clear cell renal cell carcinoma(ccRCC)immune microenvironment.Methods Transcriptome data,clinical data and mutation data were obtained from TCGA-KIRC.CIBERSORT was used to calculate the proportion of M2 macrophage cells of each case of the 539 samples.Genes associated with macrophage M2 in TCGA-KIRC were determined by intersection,and a coexpression network was established.Following LASSO regression,a prognostic model was constructed,and factors with significant findings were entered into a Cox regression analysis.Next,we used the external dataset E-MTAB-1980 from the ArrayExpress database for validation.Lastly,risk score was evaluated by stroma immune infiltration,GSEA,TMB and drug sensitivity.Results We obtained the top 46 genes most strongly correlated with macrophage M2 in TCGA-KIRC,which are enriched in immune receptor activity,leukocyte and mononuclear cell migration.A model of twelve genes related to the coexpressed macrophage M2 gene was established,and we demonstrated that it had good prognostic capacity.M2 macrophage infiltration was closely related to tumor metabolism and inversely correlated with risk score in ccRCC.Conclusion We proposed a twelve-gene Cox proportional hazard regression model associated with M2 macrophage of ccRCC that could provide a measurement method to generate prognostic scores in patients with ccRCC.